MétaCan
Menu
Back to cohort
Record W2114983966 · doi:10.4102/sajems.v9i3.1096

Facilitating the transition from the second to the first economy in South Africa’s rural areas

2014· article· en· W2114983966 on OpenAlexaff
Mohammed Jahed, R I Mirrilees, D Modise

Bibliographic record

VenueSouth African Journal of Economic and Management Sciences · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsEcoMetrix
Fundersnot available
KeywordsPovertyEconomic growthContext (archaeology)EmpowermentInformal sectorRural areaScale (ratio)BusinessPolitical scienceGeographyEconomics

Abstract

fetched live from OpenAlex

Here we describe an economic development programme termed ARISE – an acronym for African Rural Initiatives for Sustainable Environments. The programme has the potential to contribute to the simultaneous achievement of three goals to which South Africa is committed, namely: • job creation, • broad-based black economic empowerment, and • achieving expressed conservation and environmental objectives. The programme is currently being developed in the form of two ongoing pilot projects that, taken together, employ 576 previously jobless people, have created several small enterprises, and are beneficially affecting thousands of hectares of severely degraded land in South Africa’s rural areas (EOI2, 2006). The programme may be categorised in the context of South Africa’s economy as either “economic development” through poverty alleviation and in future perhaps part of the Expanded Public Works Programme (EPWP), and clearly has the potential to enable people in South Africa’s rural areas to make the transition from the “second” (informal) to the “first” (formal) economy. ARISE therefore offers an ideal opportunity for a large-scale rollout across South and southern Africa.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0040.002
Open science0.0010.009
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.200
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueSouth African Journal of Economic and Management SciencesSame topicInnovation and Socioeconomic DevelopmentFrench-language works237,207